load dataset - simple contrast (dummy coding) stim Cognitive

mount_dir = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScA/1stLevel'
mount_dir = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScA/1stLevel'
con_list = dir(fullfile(mount_dir, '*/con_0026.nii'));
spm('Defaults','fMRI')
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 24360228 bytes Loading image number: 61 Elapsed time is 224.135738 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 5943010 Bit rate: 22.50 bits

check data coverage

m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans') % display
SPM12: spm_check_registration (v7759) 20:27:49 - 26/04/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
Grouping contiguous voxels: 1 regions
ans = 1×1 cell array
{1×1 region}
drawnow; snapnow

run robfit

set(gcf,'Visible','on')
figure ('Visible', 'on');
out = robfit_parcelwise(con_data_obj);
Loading atlas: CANlab_combined_atlas_object_2018_2mm.mat Initializing nodes to match regions. Updating node response data. Updating obj.connectivity.nodes. Updating obj.connectivity.nodes. Updating region averages. Updating obj.connectivity.regions. Updating obj.connectivity.regions. __________________________________________________________________ Input image diagnostic information __________________________________________________________________ Retained 8 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 57.38% Expected 3.05 outside 95% ellipsoid, found 0 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 0 images Cases Retained 3 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 31.15% Expected 3.05 outside 95% ellipsoid, found 5 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 44 Uncorrected: 5 images Cases 7 44 50 53 57 Extracting from gray_matter_mask_sparse.img. Extracting from canonical_white_matter.img. Extracting from canonical_ventricles.img. mean_gray_matter_coverage: 1 global_d_ventricles: 0.4777 global_logp_ventricles: 2.5914 global_d_wm: 0.6215 global_logp_wm: 3.8788 gm_explained_by_csf_pvalue: 3.9172e-06 r2_explained_by_csf: 0.3052 gm_l2norm_explained_by_csf_pvalue: 0 r2_l2norm_explained_by_csf: 0.7985 csf_to_gm_signal_ratio: 1.4072 gm_scale_inhom: 0.6995 csf_scale_inhom: 0.7214 warnings: {1×6 cell} Warning: Significant global activation in CSF space/ventricles. - Effect size is d = 0.48 Warning: Significant global activation in white matter. - Effect size is d = 0.62 Warning: Gray-matter individual diffs significantly correlated with mean CSF value. - Var explained (r^2) = 30.52% Warning: Gray-matter scale (L2 norm) significantly correlated with mean CSF L2 norm. - Var explained (r^2) = 79.85% Warning: Strong non-zero signal in CSF relative to gray matter. - Ratio is = 1.41 Warning: High individual diffs in image scaling estimated from CSF. - CV is = 0.72 Number of unique values in dataset: 1 Bit rate: 0.00 bits Warning: Number of unique values in dataset is low, indicating possible restriction of bit rate. For comparison, Int16 has 65,536 unique values Number of unique values in dataset: 1 Bit rate: 0.00 bits Warning: Number of unique values in dataset is low, indicating possible restriction of bit rate. For comparison, Int16 has 65,536 unique values Number of unique values in dataset: 1 Bit rate: 0.00 bits Warning: Number of unique values in dataset is low, indicating possible restriction of bit rate. For comparison, Int16 has 65,536 unique values __________________________________________________________________ Parcel-wise robust regression __________________________________________________________________ maxT minP sig05 sig005 sig001 sigFDR05 p_thr_FDR05 min_d_FDR05 ______ __________ _____ ______ ______ ________ ___________ ___________ Intercept (Group avg) 4.5373 2.7888e-05 241 109 45 390 0.2518 0.086869 sig*: Significant parcels at given threshold (p < 0.05 two-tailed, q < 0.05 FDR, etc.) p_thr_FDR05: P-value threshold to achieve q < 0.05 FDR-corrected for each predictor min_d_FDR05: Min Cohen's d detectable at FDR q < 0.05dashes __________________________________________________________________ Tables of regions at q < 0.05 FDR __________________________________________________________________ __________________________________________________________________ Predictor 1: Intercept (Group avg) __________________________________________________________________ Grouping contiguous voxels: 4 regions
48
____________________________________________________________________________________________________________________________________________ Positive Effects Region Volume XYZ maxZ modal_label_descriptions Perc_covered_by_label Atlas_regions_covered region_index ____________________ __________ ________________ ______ ____________________________ _____________________ _____________________ ____________ {'Multiple regions'} 1.0582e+06 -6 -18 14 4.5373 {'Cerebellum' } 3 390 1 {'Ctx_STSva_R' } 8 54 0 -28 1.2225 {'Cortex_Temporal_Parietal'} 100 0 2 {'Ctx_STSva_R' } 8 60 -14 -18 1.2225 {'Cortex_Temporal_Parietal'} 100 0 3 {'Ctx_V6_R' } 8 14 -74 18 1.9652 {'Cortex_Visual_Peripheral'} 100 0 4 Negative Effects No regions to display
______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 3 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 31.15% Expected 3.05 outside 95% ellipsoid, found 5 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 44 Uncorrected: 5 images Cases 7 44 50 53 57 Retained 8 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 57.38% Expected 3.05 outside 95% ellipsoid, found 0 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 0 images Cases Mahalanobis (cov and corr, q<0.05 corrected): 1 images Outlier_count Percentage _____________ __________ global_mean 3 4.918 global_mean_to_variance 2 3.2787 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 1 1.6393 mahal_cov_uncor 5 8.1967 mahal_cov_corrected 1 1.6393 mahal_corr_uncor 0 0 mahal_corr_corrected 0 0 Overall_uncorrected 5 8.1967 Overall_corrected 1 1.6393
SPM12: spm_check_registration (v7759) 20:30:51 - 26/04/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
Grouping contiguous voxels: 1 regions
Grouping contiguous voxels: 1 regions
Grouping contiguous voxels: 1 regions